{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 27: HERC with Equal Weights within Clusters (HERC2)\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  25 of 25 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-2.0256%</td>\n",
       "      <td>0.4057%</td>\n",
       "      <td>0.4035%</td>\n",
       "      <td>1.9693%</td>\n",
       "      <td>0.0180%</td>\n",
       "      <td>0.9305%</td>\n",
       "      <td>0.3678%</td>\n",
       "      <td>0.5783%</td>\n",
       "      <td>0.9483%</td>\n",
       "      <td>-1.1954%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5881%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>2.8236%</td>\n",
       "      <td>0.9758%</td>\n",
       "      <td>0.6987%</td>\n",
       "      <td>1.7539%</td>\n",
       "      <td>-0.1730%</td>\n",
       "      <td>0.2409%</td>\n",
       "      <td>1.3734%</td>\n",
       "      <td>-1.0858%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-11.4863%</td>\n",
       "      <td>-1.5879%</td>\n",
       "      <td>0.2412%</td>\n",
       "      <td>-1.7557%</td>\n",
       "      <td>-0.7727%</td>\n",
       "      <td>-1.2473%</td>\n",
       "      <td>-0.1735%</td>\n",
       "      <td>-1.1239%</td>\n",
       "      <td>-3.5867%</td>\n",
       "      <td>-0.9551%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5547%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>0.1592%</td>\n",
       "      <td>-1.5647%</td>\n",
       "      <td>0.3108%</td>\n",
       "      <td>-1.0155%</td>\n",
       "      <td>-0.7653%</td>\n",
       "      <td>-3.0048%</td>\n",
       "      <td>-0.9034%</td>\n",
       "      <td>-2.9145%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-5.1389%</td>\n",
       "      <td>-4.1922%</td>\n",
       "      <td>-1.6573%</td>\n",
       "      <td>-2.7699%</td>\n",
       "      <td>-1.1047%</td>\n",
       "      <td>-1.9769%</td>\n",
       "      <td>-1.2207%</td>\n",
       "      <td>-0.8856%</td>\n",
       "      <td>-4.6059%</td>\n",
       "      <td>-2.5394%</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2066%</td>\n",
       "      <td>-3.0310%</td>\n",
       "      <td>-1.0410%</td>\n",
       "      <td>-3.1557%</td>\n",
       "      <td>-1.6148%</td>\n",
       "      <td>-0.2700%</td>\n",
       "      <td>-2.2845%</td>\n",
       "      <td>-2.0570%</td>\n",
       "      <td>-0.5492%</td>\n",
       "      <td>-3.0020%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.2736%</td>\n",
       "      <td>-2.2705%</td>\n",
       "      <td>-1.6037%</td>\n",
       "      <td>-2.5425%</td>\n",
       "      <td>0.1099%</td>\n",
       "      <td>-0.2241%</td>\n",
       "      <td>0.5706%</td>\n",
       "      <td>-1.6402%</td>\n",
       "      <td>-1.7642%</td>\n",
       "      <td>-0.1650%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1538%</td>\n",
       "      <td>-1.1366%</td>\n",
       "      <td>-0.7308%</td>\n",
       "      <td>-0.1448%</td>\n",
       "      <td>0.0895%</td>\n",
       "      <td>-3.3838%</td>\n",
       "      <td>-0.1117%</td>\n",
       "      <td>-1.1387%</td>\n",
       "      <td>-0.9719%</td>\n",
       "      <td>-1.1254%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-4.3383%</td>\n",
       "      <td>0.1693%</td>\n",
       "      <td>-1.6851%</td>\n",
       "      <td>-1.0215%</td>\n",
       "      <td>0.0915%</td>\n",
       "      <td>-1.1791%</td>\n",
       "      <td>0.5674%</td>\n",
       "      <td>0.5287%</td>\n",
       "      <td>0.6616%</td>\n",
       "      <td>0.0331%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6435%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9870%</td>\n",
       "      <td>-0.1450%</td>\n",
       "      <td>1.2224%</td>\n",
       "      <td>1.4570%</td>\n",
       "      <td>0.5367%</td>\n",
       "      <td>-0.4607%</td>\n",
       "      <td>0.5799%</td>\n",
       "      <td>-1.9919%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 APA       BA      BAX      BMY    CMCSA      CNP      CPB  \\\n",
       "Date                                                                         \n",
       "2016-01-05  -2.0256%  0.4057%  0.4035%  1.9693%  0.0180%  0.9305%  0.3678%   \n",
       "2016-01-06 -11.4863% -1.5879%  0.2412% -1.7557% -0.7727% -1.2473% -0.1735%   \n",
       "2016-01-07  -5.1389% -4.1922% -1.6573% -2.7699% -1.1047% -1.9769% -1.2207%   \n",
       "2016-01-08   0.2736% -2.2705% -1.6037% -2.5425%  0.1099% -0.2241%  0.5706%   \n",
       "2016-01-11  -4.3383%  0.1693% -1.6851% -1.0215%  0.0915% -1.1791%  0.5674%   \n",
       "\n",
       "                 DE      HPQ      JCI  ...       NI     PCAR      PSA  \\\n",
       "Date                                   ...                              \n",
       "2016-01-05  0.5783%  0.9483% -1.1954%  ...  1.5881%  0.0212%  2.8236%   \n",
       "2016-01-06 -1.1239% -3.5867% -0.9551%  ...  0.5547%  0.0212%  0.1592%   \n",
       "2016-01-07 -0.8856% -4.6059% -2.5394%  ... -2.2066% -3.0310% -1.0410%   \n",
       "2016-01-08 -1.6402% -1.7642% -0.1650%  ... -0.1538% -1.1366% -0.7308%   \n",
       "2016-01-11  0.5287%  0.6616%  0.0331%  ...  1.6435%  0.0000%  0.9870%   \n",
       "\n",
       "                SEE        T      TGT      TMO      TXT       VZ     ZION  \n",
       "Date                                                                       \n",
       "2016-01-05  0.9758%  0.6987%  1.7539% -0.1730%  0.2409%  1.3734% -1.0858%  \n",
       "2016-01-06 -1.5647%  0.3108% -1.0155% -0.7653% -3.0048% -0.9034% -2.9145%  \n",
       "2016-01-07 -3.1557% -1.6148% -0.2700% -2.2845% -2.0570% -0.5492% -3.0020%  \n",
       "2016-01-08 -0.1448%  0.0895% -3.3838% -0.1117% -1.1387% -0.9719% -1.1254%  \n",
       "2016-01-11 -0.1450%  1.2224%  1.4570%  0.5367% -0.4607%  0.5799% -1.9919%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "# Plotting Assets Clusters\n",
    "\n",
    "ax = rp.plot_clusters(returns=Y,\n",
    "                      codependence='pearson',\n",
    "                      linkage='ward',\n",
    "                      k=None,\n",
    "                      max_k=10,\n",
    "                      leaf_order=True,\n",
    "                      dendrogram=True,\n",
    "                      #linecolor='tab:purple',\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above suggest that optimal number of clusters are four.\n",
    "\n",
    "## 2. Estimating HERC and HERC2 Portfolios\n",
    "\n",
    "The HERC2 portfolio is just a modification of HERC portfolio making the weights within clusters equal and no based on naive risk parity like original HERC portfolio.\n",
    "\n",
    "### 2.1 Calculating the HERC and HERC2 portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_8b268_row20_col0, #T_8b268_row21_col0 {\n",
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       "</style>\n",
       "<table id=\"T_8b268\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8b268_level0_col0\" class=\"col_heading level0 col0\" >HERC</th>\n",
       "      <th id=\"T_8b268_level0_col1\" class=\"col_heading level0 col1\" >HERC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row0\" class=\"row_heading level0 row0\" >VZ</th>\n",
       "      <td id=\"T_8b268_row0_col0\" class=\"data row0 col0\" >8.79%</td>\n",
       "      <td id=\"T_8b268_row0_col1\" class=\"data row0 col1\" >6.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row1\" class=\"row_heading level0 row1\" >T</th>\n",
       "      <td id=\"T_8b268_row1_col0\" class=\"data row1 col0\" >7.74%</td>\n",
       "      <td id=\"T_8b268_row1_col1\" class=\"data row1 col1\" >6.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row2\" class=\"row_heading level0 row2\" >CNP</th>\n",
       "      <td id=\"T_8b268_row2_col0\" class=\"data row2 col0\" >7.64%</td>\n",
       "      <td id=\"T_8b268_row2_col1\" class=\"data row2 col1\" >6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row3\" class=\"row_heading level0 row3\" >NI</th>\n",
       "      <td id=\"T_8b268_row3_col0\" class=\"data row3 col0\" >7.37%</td>\n",
       "      <td id=\"T_8b268_row3_col1\" class=\"data row3 col1\" >6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row4\" class=\"row_heading level0 row4\" >MMC</th>\n",
       "      <td id=\"T_8b268_row4_col0\" class=\"data row4 col0\" >7.24%</td>\n",
       "      <td id=\"T_8b268_row4_col1\" class=\"data row4 col1\" >4.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row5\" class=\"row_heading level0 row5\" >PSA</th>\n",
       "      <td id=\"T_8b268_row5_col0\" class=\"data row5 col0\" >6.94%</td>\n",
       "      <td id=\"T_8b268_row5_col1\" class=\"data row5 col1\" >6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row6\" class=\"row_heading level0 row6\" >CMCSA</th>\n",
       "      <td id=\"T_8b268_row6_col0\" class=\"data row6 col0\" >5.84%</td>\n",
       "      <td id=\"T_8b268_row6_col1\" class=\"data row6 col1\" >6.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row7\" class=\"row_heading level0 row7\" >MO</th>\n",
       "      <td id=\"T_8b268_row7_col0\" class=\"data row7 col0\" >5.77%</td>\n",
       "      <td id=\"T_8b268_row7_col1\" class=\"data row7 col1\" >6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row8\" class=\"row_heading level0 row8\" >BAX</th>\n",
       "      <td id=\"T_8b268_row8_col0\" class=\"data row8 col0\" >4.55%</td>\n",
       "      <td id=\"T_8b268_row8_col1\" class=\"data row8 col1\" >4.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row9\" class=\"row_heading level0 row9\" >TMO</th>\n",
       "      <td id=\"T_8b268_row9_col0\" class=\"data row9 col0\" >4.05%</td>\n",
       "      <td id=\"T_8b268_row9_col1\" class=\"data row9 col1\" >4.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row10\" class=\"row_heading level0 row10\" >CPB</th>\n",
       "      <td id=\"T_8b268_row10_col0\" class=\"data row10 col0\" >3.90%</td>\n",
       "      <td id=\"T_8b268_row10_col1\" class=\"data row10 col1\" >6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row11\" class=\"row_heading level0 row11\" >MSFT</th>\n",
       "      <td id=\"T_8b268_row11_col0\" class=\"data row11 col0\" >3.70%</td>\n",
       "      <td id=\"T_8b268_row11_col1\" class=\"data row11 col1\" >4.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row12\" class=\"row_heading level0 row12\" >TGT</th>\n",
       "      <td id=\"T_8b268_row12_col0\" class=\"data row12 col0\" >3.19%</td>\n",
       "      <td id=\"T_8b268_row12_col1\" class=\"data row12 col1\" >6.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row13\" class=\"row_heading level0 row13\" >JPM</th>\n",
       "      <td id=\"T_8b268_row13_col0\" class=\"data row13 col0\" >2.75%</td>\n",
       "      <td id=\"T_8b268_row13_col1\" class=\"data row13 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row14\" class=\"row_heading level0 row14\" >BMY</th>\n",
       "      <td id=\"T_8b268_row14_col0\" class=\"data row14 col0\" >2.65%</td>\n",
       "      <td id=\"T_8b268_row14_col1\" class=\"data row14 col1\" >4.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row15\" class=\"row_heading level0 row15\" >HPQ</th>\n",
       "      <td id=\"T_8b268_row15_col0\" class=\"data row15 col0\" >2.25%</td>\n",
       "      <td id=\"T_8b268_row15_col1\" class=\"data row15 col1\" >4.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row16\" class=\"row_heading level0 row16\" >JCI</th>\n",
       "      <td id=\"T_8b268_row16_col0\" class=\"data row16 col0\" >2.25%</td>\n",
       "      <td id=\"T_8b268_row16_col1\" class=\"data row16 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row17\" class=\"row_heading level0 row17\" >PCAR</th>\n",
       "      <td id=\"T_8b268_row17_col0\" class=\"data row17 col0\" >2.12%</td>\n",
       "      <td id=\"T_8b268_row17_col1\" class=\"data row17 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_8b268_row18_col0\" class=\"data row18 col0\" >2.02%</td>\n",
       "      <td id=\"T_8b268_row18_col1\" class=\"data row18 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row19\" class=\"row_heading level0 row19\" >TXT</th>\n",
       "      <td id=\"T_8b268_row19_col0\" class=\"data row19 col0\" >1.85%</td>\n",
       "      <td id=\"T_8b268_row19_col1\" class=\"data row19 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row20\" class=\"row_heading level0 row20\" >DE</th>\n",
       "      <td id=\"T_8b268_row20_col0\" class=\"data row20 col0\" >1.73%</td>\n",
       "      <td id=\"T_8b268_row20_col1\" class=\"data row20 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row21\" class=\"row_heading level0 row21\" >BA</th>\n",
       "      <td id=\"T_8b268_row21_col0\" class=\"data row21 col0\" >1.73%</td>\n",
       "      <td id=\"T_8b268_row21_col1\" class=\"data row21 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row22\" class=\"row_heading level0 row22\" >LUV</th>\n",
       "      <td id=\"T_8b268_row22_col0\" class=\"data row22 col0\" >1.66%</td>\n",
       "      <td id=\"T_8b268_row22_col1\" class=\"data row22 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row23\" class=\"row_heading level0 row23\" >ZION</th>\n",
       "      <td id=\"T_8b268_row23_col0\" class=\"data row23 col0\" >1.63%</td>\n",
       "      <td id=\"T_8b268_row23_col1\" class=\"data row23 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b268_level0_row24\" class=\"row_heading level0 row24\" >APA</th>\n",
       "      <td id=\"T_8b268_row24_col0\" class=\"data row24 col0\" >0.64%</td>\n",
       "      <td id=\"T_8b268_row24_col1\" class=\"data row24 col1\" >1.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1774c24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building the portfolio object\n",
    "port = rp.HCPortfolio(returns=Y)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "codependence = 'pearson' # Correlation matrix used to group assets in clusters\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "rf = 0 # Risk free rate\n",
    "linkage = 'ward' # Linkage method used to build clusters\n",
    "max_k = 10 # Max number of clusters used in two difference gap statistic\n",
    "leaf_order = True # Consider optimal order of leafs in dendrogram\n",
    "\n",
    "w1 = port.optimization(model='HERC',\n",
    "                       codependence=codependence,\n",
    "                       method_cov='hist',\n",
    "                       rm=rm,\n",
    "                       rf=rf,\n",
    "                       linkage=linkage,\n",
    "                       max_k=max_k,\n",
    "                       leaf_order=leaf_order)\n",
    "\n",
    "w2 = port.optimization(model='HERC2',\n",
    "                       codependence=codependence,\n",
    "                       method_cov='hist',\n",
    "                       rm=rm,\n",
    "                       rf=rf,\n",
    "                       linkage=linkage,\n",
    "                       max_k=max_k,\n",
    "                       leaf_order=leaf_order)\n",
    "\n",
    "w = pd.concat([w1, w2], axis=1)\n",
    "w.columns = ['HERC', 'HERC2']\n",
    "\n",
    "display(w.sort_values(by='HERC', ascending=False).style.format(\"{:.2%}\").background_gradient(cmap='YlGn'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting Risk Contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Risk (Standard Deviation) Contribution per Asset'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the risk contribution per asset\n",
    "\n",
    "mu = Y.mean()\n",
    "cov = Y.cov() # Covariance matrix\n",
    "returns = Y # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(2,1, figsize=(12, 12))\n",
    "\n",
    "ax = np.ravel(ax)\n",
    "rp.plot_risk_con(w=w1,\n",
    "                 cov=cov,\n",
    "                 returns=returns,\n",
    "                 rm=rm,\n",
    "                 rf=0,\n",
    "                 alpha=0.05,\n",
    "                 color=\"tab:blue\",\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 t_factor=252,\n",
    "                 ax=ax[0])\n",
    "\n",
    "rp.plot_risk_con(w=w2,\n",
    "                 cov=cov,\n",
    "                 returns=returns,\n",
    "                 rm=rm,\n",
    "                 rf=0,\n",
    "                 alpha=0.05,\n",
    "                 color=\"tab:blue\",\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 t_factor=252,\n",
    "                 ax=ax[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate Optimal HERC and HERC2 Portfolios for Several Covariance Estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Covariance estimators available:\n",
    "#\n",
    "# 'hist': use historical estimates.\n",
    "# 'ewma1'': use ewma with adjust=True, see `EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html#exponentially-weighted-windows>`_ for more details.\n",
    "# 'ewma2': use ewma with adjust=False, see `EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html#exponentially-weighted-windows>`_ for more details.\n",
    "# 'ledoit': use the Ledoit and Wolf Shrinkage method.\n",
    "# 'oas': use the Oracle Approximation Shrinkage method.\n",
    "# 'shrunk': use the basic Shrunk Covariance method.\n",
    "\n",
    "models = ['HERC'] * 6 + ['HERC2'] * 6\n",
    "covariances = ['hist', 'ewma1', 'ewma2', 'ledoit', 'oas', 'shrunk'] * 2\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "for i,j in zip(models, covariances):\n",
    "    w = port.optimization(model=i,\n",
    "                          codependence=codependence,\n",
    "                          method_cov=j,\n",
    "                          rm=rm,\n",
    "                          rf=rf,\n",
    "                          linkage=linkage,\n",
    "                          max_k=max_k,\n",
    "                          leaf_order=leaf_order)\n",
    "\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = zip(models, covariances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "#T_37a40_row2_col11, #T_37a40_row3_col11, #T_37a40_row8_col11, #T_37a40_row12_col11, #T_37a40_row14_col11, #T_37a40_row21_col11 {\n",
       "  background-color: #79c679;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row3_col0, #T_37a40_row3_col4, #T_37a40_row10_col1, #T_37a40_row10_col2 {\n",
       "  background-color: #daf0a4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row3_col1, #T_37a40_row3_col2 {\n",
       "  background-color: #c9e99c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row3_col3 {\n",
       "  background-color: #d9f0a3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row3_col5 {\n",
       "  background-color: #d0ec9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row4_col0, #T_37a40_row13_col3 {\n",
       "  background-color: #3ea75a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row4_col1, #T_37a40_row4_col2, #T_37a40_row6_col0 {\n",
       "  background-color: #a2d88a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row4_col3 {\n",
       "  background-color: #3ca458;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row4_col4 {\n",
       "  background-color: #3da559;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row4_col5, #T_37a40_row12_col1, #T_37a40_row12_col2 {\n",
       "  background-color: #349a52;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row4_col6, #T_37a40_row4_col7, #T_37a40_row4_col8, #T_37a40_row4_col9, #T_37a40_row4_col10, #T_37a40_row4_col11, #T_37a40_row19_col6, #T_37a40_row19_col7, #T_37a40_row19_col8, #T_37a40_row19_col9, #T_37a40_row19_col10, #T_37a40_row19_col11, #T_37a40_row20_col6, #T_37a40_row20_col7, #T_37a40_row20_col8, #T_37a40_row20_col9, #T_37a40_row20_col10, #T_37a40_row20_col11, #T_37a40_row23_col0, #T_37a40_row23_col1, #T_37a40_row23_col2, #T_37a40_row23_col3, #T_37a40_row23_col4, #T_37a40_row23_col5, #T_37a40_row23_col6, #T_37a40_row23_col7, #T_37a40_row23_col8, #T_37a40_row23_col9, #T_37a40_row23_col10, #T_37a40_row23_col11 {\n",
       "  background-color: #004529;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row5_col0 {\n",
       "  background-color: #056c39;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row5_col1, #T_37a40_row5_col2, #T_37a40_row8_col3 {\n",
       "  background-color: #e4f4ab;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row5_col3, #T_37a40_row5_col4 {\n",
       "  background-color: #036b38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row5_col5, #T_37a40_row19_col0, #T_37a40_row19_col4 {\n",
       "  background-color: #006837;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row5_col6, #T_37a40_row5_col9, #T_37a40_row5_col10, #T_37a40_row6_col6, #T_37a40_row6_col9, #T_37a40_row6_col10, #T_37a40_row13_col6, #T_37a40_row13_col9, #T_37a40_row13_col10, #T_37a40_row15_col6, #T_37a40_row15_col9, #T_37a40_row15_col10, #T_37a40_row17_col6, #T_37a40_row17_col9, #T_37a40_row17_col10 {\n",
       "  background-color: #00482a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row5_col7, #T_37a40_row5_col8, #T_37a40_row6_col7, #T_37a40_row6_col8, #T_37a40_row13_col7, #T_37a40_row13_col8, #T_37a40_row15_col7, #T_37a40_row15_col8, #T_37a40_row17_col0, #T_37a40_row17_col4, #T_37a40_row17_col7, #T_37a40_row17_col8 {\n",
       "  background-color: #1c7e40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row5_col11, #T_37a40_row6_col11, #T_37a40_row13_col11, #T_37a40_row15_col11, #T_37a40_row17_col11 {\n",
       "  background-color: #00472a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row6_col3, #T_37a40_row14_col5 {\n",
       "  background-color: #9fd788;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row6_col4 {\n",
       "  background-color: #a1d889;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row6_col5 {\n",
       "  background-color: #90d083;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row8_col0, #T_37a40_row9_col0, #T_37a40_row9_col4 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row8_col1, #T_37a40_row8_col2 {\n",
       "  background-color: #e2f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row8_col4, #T_37a40_row9_col3 {\n",
       "  background-color: #e5f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row8_col5, #T_37a40_row16_col1, #T_37a40_row16_col2 {\n",
       "  background-color: #dff3a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row9_col1, #T_37a40_row9_col2, #T_37a40_row22_col5 {\n",
       "  background-color: #eef9b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row9_col5, #T_37a40_row11_col1, #T_37a40_row11_col2 {\n",
       "  background-color: #e3f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row10_col0, #T_37a40_row10_col3, #T_37a40_row10_col4 {\n",
       "  background-color: #d6efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row10_col5 {\n",
       "  background-color: #d3eda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row11_col0, #T_37a40_row24_col3, #T_37a40_row24_col4 {\n",
       "  background-color: #f7fcba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row11_col3, #T_37a40_row11_col4 {\n",
       "  background-color: #f7fcb9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row12_col0 {\n",
       "  background-color: #12763d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row12_col3, #T_37a40_row12_col4 {\n",
       "  background-color: #13773d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row12_col5 {\n",
       "  background-color: #187b3f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row13_col0 {\n",
       "  background-color: #3fa95c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row13_col1, #T_37a40_row13_col2 {\n",
       "  background-color: #95d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row13_col4 {\n",
       "  background-color: #3fa85b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row13_col5 {\n",
       "  background-color: #379e54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row14_col0 {\n",
       "  background-color: #acdd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row14_col1, #T_37a40_row14_col2, #T_37a40_row15_col1, #T_37a40_row15_col2 {\n",
       "  background-color: #278946;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row14_col3, #T_37a40_row14_col4 {\n",
       "  background-color: #abdc8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row15_col0 {\n",
       "  background-color: #0d733c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row15_col3, #T_37a40_row15_col4 {\n",
       "  background-color: #0c723b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row15_col5 {\n",
       "  background-color: #096f3a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row16_col0, #T_37a40_row16_col4, #T_37a40_row18_col5 {\n",
       "  background-color: #eaf7af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row16_col3 {\n",
       "  background-color: #e9f6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row16_col5 {\n",
       "  background-color: #e7f6ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row17_col3 {\n",
       "  background-color: #1a7d40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row17_col5 {\n",
       "  background-color: #15793e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row18_col0, #T_37a40_row18_col3, #T_37a40_row18_col4 {\n",
       "  background-color: #edf8b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row18_col1, #T_37a40_row18_col2 {\n",
       "  background-color: #f9fdc2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row19_col1, #T_37a40_row19_col2 {\n",
       "  background-color: #288a47;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row19_col3 {\n",
       "  background-color: #006737;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row19_col5 {\n",
       "  background-color: #006435;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_37a40_row20_col0 {\n",
       "  background-color: #c4e799;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row20_col1, #T_37a40_row20_col2, #T_37a40_row22_col3, #T_37a40_row22_col4 {\n",
       "  background-color: #f1fab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row20_col3 {\n",
       "  background-color: #c0e597;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row20_col4 {\n",
       "  background-color: #c1e698;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row20_col5 {\n",
       "  background-color: #b5e092;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row21_col0 {\n",
       "  background-color: #9ad587;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row21_col1, #T_37a40_row21_col2 {\n",
       "  background-color: #a7db8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row21_col3, #T_37a40_row21_col4 {\n",
       "  background-color: #98d486;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row21_col5 {\n",
       "  background-color: #8ed082;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row22_col1, #T_37a40_row22_col2 {\n",
       "  background-color: #f8fcbe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row24_col0 {\n",
       "  background-color: #f7fcbc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_37a40_row24_col5 {\n",
       "  background-color: #f6fcb8;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_37a40\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_37a40_level0_col0\" class=\"col_heading level0 col0\" >('HERC', 'hist')</th>\n",
       "      <th id=\"T_37a40_level0_col1\" class=\"col_heading level0 col1\" >('HERC', 'ewma1')</th>\n",
       "      <th id=\"T_37a40_level0_col2\" class=\"col_heading level0 col2\" >('HERC', 'ewma2')</th>\n",
       "      <th id=\"T_37a40_level0_col3\" class=\"col_heading level0 col3\" >('HERC', 'ledoit')</th>\n",
       "      <th id=\"T_37a40_level0_col4\" class=\"col_heading level0 col4\" >('HERC', 'oas')</th>\n",
       "      <th id=\"T_37a40_level0_col5\" class=\"col_heading level0 col5\" >('HERC', 'shrunk')</th>\n",
       "      <th id=\"T_37a40_level0_col6\" class=\"col_heading level0 col6\" >('HERC2', 'hist')</th>\n",
       "      <th id=\"T_37a40_level0_col7\" class=\"col_heading level0 col7\" >('HERC2', 'ewma1')</th>\n",
       "      <th id=\"T_37a40_level0_col8\" class=\"col_heading level0 col8\" >('HERC2', 'ewma2')</th>\n",
       "      <th id=\"T_37a40_level0_col9\" class=\"col_heading level0 col9\" >('HERC2', 'ledoit')</th>\n",
       "      <th id=\"T_37a40_level0_col10\" class=\"col_heading level0 col10\" >('HERC2', 'oas')</th>\n",
       "      <th id=\"T_37a40_level0_col11\" class=\"col_heading level0 col11\" >('HERC2', 'shrunk')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_37a40_row0_col0\" class=\"data row0 col0\" >0.64%</td>\n",
       "      <td id=\"T_37a40_row0_col1\" class=\"data row0 col1\" >0.09%</td>\n",
       "      <td id=\"T_37a40_row0_col2\" class=\"data row0 col2\" >0.09%</td>\n",
       "      <td id=\"T_37a40_row0_col3\" class=\"data row0 col3\" >0.66%</td>\n",
       "      <td id=\"T_37a40_row0_col4\" class=\"data row0 col4\" >0.65%</td>\n",
       "      <td id=\"T_37a40_row0_col5\" class=\"data row0 col5\" >0.72%</td>\n",
       "      <td id=\"T_37a40_row0_col6\" class=\"data row0 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row0_col7\" class=\"data row0 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row0_col8\" class=\"data row0 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row0_col9\" class=\"data row0 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row0_col10\" class=\"data row0 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row0_col11\" class=\"data row0 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_37a40_row1_col0\" class=\"data row1 col0\" >1.73%</td>\n",
       "      <td id=\"T_37a40_row1_col1\" class=\"data row1 col1\" >0.77%</td>\n",
       "      <td id=\"T_37a40_row1_col2\" class=\"data row1 col2\" >0.77%</td>\n",
       "      <td id=\"T_37a40_row1_col3\" class=\"data row1 col3\" >1.75%</td>\n",
       "      <td id=\"T_37a40_row1_col4\" class=\"data row1 col4\" >1.74%</td>\n",
       "      <td id=\"T_37a40_row1_col5\" class=\"data row1 col5\" >1.83%</td>\n",
       "      <td id=\"T_37a40_row1_col6\" class=\"data row1 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row1_col7\" class=\"data row1 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row1_col8\" class=\"data row1 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row1_col9\" class=\"data row1 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row1_col10\" class=\"data row1 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row1_col11\" class=\"data row1 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_37a40_row2_col0\" class=\"data row2 col0\" >4.55%</td>\n",
       "      <td id=\"T_37a40_row2_col1\" class=\"data row2 col1\" >2.60%</td>\n",
       "      <td id=\"T_37a40_row2_col2\" class=\"data row2 col2\" >2.60%</td>\n",
       "      <td id=\"T_37a40_row2_col3\" class=\"data row2 col3\" >4.56%</td>\n",
       "      <td id=\"T_37a40_row2_col4\" class=\"data row2 col4\" >4.56%</td>\n",
       "      <td id=\"T_37a40_row2_col5\" class=\"data row2 col5\" >4.58%</td>\n",
       "      <td id=\"T_37a40_row2_col6\" class=\"data row2 col6\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row2_col7\" class=\"data row2 col7\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row2_col8\" class=\"data row2 col8\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row2_col9\" class=\"data row2 col9\" >4.08%</td>\n",
       "      <td id=\"T_37a40_row2_col10\" class=\"data row2 col10\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row2_col11\" class=\"data row2 col11\" >4.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_37a40_row3_col0\" class=\"data row3 col0\" >2.65%</td>\n",
       "      <td id=\"T_37a40_row3_col1\" class=\"data row3 col1\" >3.45%</td>\n",
       "      <td id=\"T_37a40_row3_col2\" class=\"data row3 col2\" >3.45%</td>\n",
       "      <td id=\"T_37a40_row3_col3\" class=\"data row3 col3\" >2.69%</td>\n",
       "      <td id=\"T_37a40_row3_col4\" class=\"data row3 col4\" >2.67%</td>\n",
       "      <td id=\"T_37a40_row3_col5\" class=\"data row3 col5\" >2.83%</td>\n",
       "      <td id=\"T_37a40_row3_col6\" class=\"data row3 col6\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row3_col7\" class=\"data row3 col7\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row3_col8\" class=\"data row3 col8\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row3_col9\" class=\"data row3 col9\" >4.08%</td>\n",
       "      <td id=\"T_37a40_row3_col10\" class=\"data row3 col10\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row3_col11\" class=\"data row3 col11\" >4.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_37a40_row4_col0\" class=\"data row4 col0\" >5.84%</td>\n",
       "      <td id=\"T_37a40_row4_col1\" class=\"data row4 col1\" >4.64%</td>\n",
       "      <td id=\"T_37a40_row4_col2\" class=\"data row4 col2\" >4.64%</td>\n",
       "      <td id=\"T_37a40_row4_col3\" class=\"data row4 col3\" >5.85%</td>\n",
       "      <td id=\"T_37a40_row4_col4\" class=\"data row4 col4\" >5.85%</td>\n",
       "      <td id=\"T_37a40_row4_col5\" class=\"data row4 col5\" >5.88%</td>\n",
       "      <td id=\"T_37a40_row4_col6\" class=\"data row4 col6\" >6.39%</td>\n",
       "      <td id=\"T_37a40_row4_col7\" class=\"data row4 col7\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row4_col8\" class=\"data row4 col8\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row4_col9\" class=\"data row4 col9\" >6.36%</td>\n",
       "      <td id=\"T_37a40_row4_col10\" class=\"data row4 col10\" >6.37%</td>\n",
       "      <td id=\"T_37a40_row4_col11\" class=\"data row4 col11\" >6.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_37a40_row5_col0\" class=\"data row5 col0\" >7.64%</td>\n",
       "      <td id=\"T_37a40_row5_col1\" class=\"data row5 col1\" >2.45%</td>\n",
       "      <td id=\"T_37a40_row5_col2\" class=\"data row5 col2\" >2.45%</td>\n",
       "      <td id=\"T_37a40_row5_col3\" class=\"data row5 col3\" >7.58%</td>\n",
       "      <td id=\"T_37a40_row5_col4\" class=\"data row5 col4\" >7.60%</td>\n",
       "      <td id=\"T_37a40_row5_col5\" class=\"data row5 col5\" >7.36%</td>\n",
       "      <td id=\"T_37a40_row5_col6\" class=\"data row5 col6\" >6.33%</td>\n",
       "      <td id=\"T_37a40_row5_col7\" class=\"data row5 col7\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row5_col8\" class=\"data row5 col8\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row5_col9\" class=\"data row5 col9\" >6.31%</td>\n",
       "      <td id=\"T_37a40_row5_col10\" class=\"data row5 col10\" >6.32%</td>\n",
       "      <td id=\"T_37a40_row5_col11\" class=\"data row5 col11\" >6.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_37a40_row6_col0\" class=\"data row6 col0\" >3.90%</td>\n",
       "      <td id=\"T_37a40_row6_col1\" class=\"data row6 col1\" >5.62%</td>\n",
       "      <td id=\"T_37a40_row6_col2\" class=\"data row6 col2\" >5.62%</td>\n",
       "      <td id=\"T_37a40_row6_col3\" class=\"data row6 col3\" >3.95%</td>\n",
       "      <td id=\"T_37a40_row6_col4\" class=\"data row6 col4\" >3.93%</td>\n",
       "      <td id=\"T_37a40_row6_col5\" class=\"data row6 col5\" >4.09%</td>\n",
       "      <td id=\"T_37a40_row6_col6\" class=\"data row6 col6\" >6.33%</td>\n",
       "      <td id=\"T_37a40_row6_col7\" class=\"data row6 col7\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row6_col8\" class=\"data row6 col8\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row6_col9\" class=\"data row6 col9\" >6.31%</td>\n",
       "      <td id=\"T_37a40_row6_col10\" class=\"data row6 col10\" >6.32%</td>\n",
       "      <td id=\"T_37a40_row6_col11\" class=\"data row6 col11\" >6.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_37a40_row7_col0\" class=\"data row7 col0\" >1.73%</td>\n",
       "      <td id=\"T_37a40_row7_col1\" class=\"data row7 col1\" >1.76%</td>\n",
       "      <td id=\"T_37a40_row7_col2\" class=\"data row7 col2\" >1.76%</td>\n",
       "      <td id=\"T_37a40_row7_col3\" class=\"data row7 col3\" >1.75%</td>\n",
       "      <td id=\"T_37a40_row7_col4\" class=\"data row7 col4\" >1.75%</td>\n",
       "      <td id=\"T_37a40_row7_col5\" class=\"data row7 col5\" >1.83%</td>\n",
       "      <td id=\"T_37a40_row7_col6\" class=\"data row7 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row7_col7\" class=\"data row7 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row7_col8\" class=\"data row7 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row7_col9\" class=\"data row7 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row7_col10\" class=\"data row7 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row7_col11\" class=\"data row7 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_37a40_row8_col0\" class=\"data row8 col0\" >2.25%</td>\n",
       "      <td id=\"T_37a40_row8_col1\" class=\"data row8 col1\" >2.50%</td>\n",
       "      <td id=\"T_37a40_row8_col2\" class=\"data row8 col2\" >2.50%</td>\n",
       "      <td id=\"T_37a40_row8_col3\" class=\"data row8 col3\" >2.29%</td>\n",
       "      <td id=\"T_37a40_row8_col4\" class=\"data row8 col4\" >2.28%</td>\n",
       "      <td id=\"T_37a40_row8_col5\" class=\"data row8 col5\" >2.44%</td>\n",
       "      <td id=\"T_37a40_row8_col6\" class=\"data row8 col6\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row8_col7\" class=\"data row8 col7\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row8_col8\" class=\"data row8 col8\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row8_col9\" class=\"data row8 col9\" >4.08%</td>\n",
       "      <td id=\"T_37a40_row8_col10\" class=\"data row8 col10\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row8_col11\" class=\"data row8 col11\" >4.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_37a40_row9_col0\" class=\"data row9 col0\" >2.25%</td>\n",
       "      <td id=\"T_37a40_row9_col1\" class=\"data row9 col1\" >1.94%</td>\n",
       "      <td id=\"T_37a40_row9_col2\" class=\"data row9 col2\" >1.94%</td>\n",
       "      <td id=\"T_37a40_row9_col3\" class=\"data row9 col3\" >2.26%</td>\n",
       "      <td id=\"T_37a40_row9_col4\" class=\"data row9 col4\" >2.26%</td>\n",
       "      <td id=\"T_37a40_row9_col5\" class=\"data row9 col5\" >2.32%</td>\n",
       "      <td id=\"T_37a40_row9_col6\" class=\"data row9 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row9_col7\" class=\"data row9 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row9_col8\" class=\"data row9 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row9_col9\" class=\"data row9 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row9_col10\" class=\"data row9 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row9_col11\" class=\"data row9 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_37a40_row10_col0\" class=\"data row10 col0\" >2.75%</td>\n",
       "      <td id=\"T_37a40_row10_col1\" class=\"data row10 col1\" >2.90%</td>\n",
       "      <td id=\"T_37a40_row10_col2\" class=\"data row10 col2\" >2.90%</td>\n",
       "      <td id=\"T_37a40_row10_col3\" class=\"data row10 col3\" >2.75%</td>\n",
       "      <td id=\"T_37a40_row10_col4\" class=\"data row10 col4\" >2.75%</td>\n",
       "      <td id=\"T_37a40_row10_col5\" class=\"data row10 col5\" >2.76%</td>\n",
       "      <td id=\"T_37a40_row10_col6\" class=\"data row10 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row10_col7\" class=\"data row10 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row10_col8\" class=\"data row10 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row10_col9\" class=\"data row10 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row10_col10\" class=\"data row10 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row10_col11\" class=\"data row10 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_37a40_row11_col0\" class=\"data row11 col0\" >1.66%</td>\n",
       "      <td id=\"T_37a40_row11_col1\" class=\"data row11 col1\" >2.47%</td>\n",
       "      <td id=\"T_37a40_row11_col2\" class=\"data row11 col2\" >2.47%</td>\n",
       "      <td id=\"T_37a40_row11_col3\" class=\"data row11 col3\" >1.68%</td>\n",
       "      <td id=\"T_37a40_row11_col4\" class=\"data row11 col4\" >1.67%</td>\n",
       "      <td id=\"T_37a40_row11_col5\" class=\"data row11 col5\" >1.76%</td>\n",
       "      <td id=\"T_37a40_row11_col6\" class=\"data row11 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row11_col7\" class=\"data row11 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row11_col8\" class=\"data row11 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row11_col9\" class=\"data row11 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row11_col10\" class=\"data row11 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row11_col11\" class=\"data row11 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_37a40_row12_col0\" class=\"data row12 col0\" >7.24%</td>\n",
       "      <td id=\"T_37a40_row12_col1\" class=\"data row12 col1\" >7.81%</td>\n",
       "      <td id=\"T_37a40_row12_col2\" class=\"data row12 col2\" >7.81%</td>\n",
       "      <td id=\"T_37a40_row12_col3\" class=\"data row12 col3\" >7.12%</td>\n",
       "      <td id=\"T_37a40_row12_col4\" class=\"data row12 col4\" >7.17%</td>\n",
       "      <td id=\"T_37a40_row12_col5\" class=\"data row12 col5\" >6.72%</td>\n",
       "      <td id=\"T_37a40_row12_col6\" class=\"data row12 col6\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row12_col7\" class=\"data row12 col7\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row12_col8\" class=\"data row12 col8\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row12_col9\" class=\"data row12 col9\" >4.08%</td>\n",
       "      <td id=\"T_37a40_row12_col10\" class=\"data row12 col10\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row12_col11\" class=\"data row12 col11\" >4.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_37a40_row13_col0\" class=\"data row13 col0\" >5.77%</td>\n",
       "      <td id=\"T_37a40_row13_col1\" class=\"data row13 col1\" >5.01%</td>\n",
       "      <td id=\"T_37a40_row13_col2\" class=\"data row13 col2\" >5.01%</td>\n",
       "      <td id=\"T_37a40_row13_col3\" class=\"data row13 col3\" >5.78%</td>\n",
       "      <td id=\"T_37a40_row13_col4\" class=\"data row13 col4\" >5.78%</td>\n",
       "      <td id=\"T_37a40_row13_col5\" class=\"data row13 col5\" >5.80%</td>\n",
       "      <td id=\"T_37a40_row13_col6\" class=\"data row13 col6\" >6.33%</td>\n",
       "      <td id=\"T_37a40_row13_col7\" class=\"data row13 col7\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row13_col8\" class=\"data row13 col8\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row13_col9\" class=\"data row13 col9\" >6.31%</td>\n",
       "      <td id=\"T_37a40_row13_col10\" class=\"data row13 col10\" >6.32%</td>\n",
       "      <td id=\"T_37a40_row13_col11\" class=\"data row13 col11\" >6.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_37a40_row14_col0\" class=\"data row14 col0\" >3.70%</td>\n",
       "      <td id=\"T_37a40_row14_col1\" class=\"data row14 col1\" >8.45%</td>\n",
       "      <td id=\"T_37a40_row14_col2\" class=\"data row14 col2\" >8.45%</td>\n",
       "      <td id=\"T_37a40_row14_col3\" class=\"data row14 col3\" >3.73%</td>\n",
       "      <td id=\"T_37a40_row14_col4\" class=\"data row14 col4\" >3.71%</td>\n",
       "      <td id=\"T_37a40_row14_col5\" class=\"data row14 col5\" >3.82%</td>\n",
       "      <td id=\"T_37a40_row14_col6\" class=\"data row14 col6\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row14_col7\" class=\"data row14 col7\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row14_col8\" class=\"data row14 col8\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row14_col9\" class=\"data row14 col9\" >4.08%</td>\n",
       "      <td id=\"T_37a40_row14_col10\" class=\"data row14 col10\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row14_col11\" class=\"data row14 col11\" >4.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_37a40_row15_col0\" class=\"data row15 col0\" >7.37%</td>\n",
       "      <td id=\"T_37a40_row15_col1\" class=\"data row15 col1\" >8.44%</td>\n",
       "      <td id=\"T_37a40_row15_col2\" class=\"data row15 col2\" >8.44%</td>\n",
       "      <td id=\"T_37a40_row15_col3\" class=\"data row15 col3\" >7.32%</td>\n",
       "      <td id=\"T_37a40_row15_col4\" class=\"data row15 col4\" >7.34%</td>\n",
       "      <td id=\"T_37a40_row15_col5\" class=\"data row15 col5\" >7.15%</td>\n",
       "      <td id=\"T_37a40_row15_col6\" class=\"data row15 col6\" >6.33%</td>\n",
       "      <td id=\"T_37a40_row15_col7\" class=\"data row15 col7\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row15_col8\" class=\"data row15 col8\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row15_col9\" class=\"data row15 col9\" >6.31%</td>\n",
       "      <td id=\"T_37a40_row15_col10\" class=\"data row15 col10\" >6.32%</td>\n",
       "      <td id=\"T_37a40_row15_col11\" class=\"data row15 col11\" >6.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_37a40_row16_col0\" class=\"data row16 col0\" >2.12%</td>\n",
       "      <td id=\"T_37a40_row16_col1\" class=\"data row16 col1\" >2.66%</td>\n",
       "      <td id=\"T_37a40_row16_col2\" class=\"data row16 col2\" >2.66%</td>\n",
       "      <td id=\"T_37a40_row16_col3\" class=\"data row16 col3\" >2.14%</td>\n",
       "      <td id=\"T_37a40_row16_col4\" class=\"data row16 col4\" >2.13%</td>\n",
       "      <td id=\"T_37a40_row16_col5\" class=\"data row16 col5\" >2.20%</td>\n",
       "      <td id=\"T_37a40_row16_col6\" class=\"data row16 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row16_col7\" class=\"data row16 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row16_col8\" class=\"data row16 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row16_col9\" class=\"data row16 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row16_col10\" class=\"data row16 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row16_col11\" class=\"data row16 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_37a40_row17_col0\" class=\"data row17 col0\" >6.94%</td>\n",
       "      <td id=\"T_37a40_row17_col1\" class=\"data row17 col1\" >5.88%</td>\n",
       "      <td id=\"T_37a40_row17_col2\" class=\"data row17 col2\" >5.88%</td>\n",
       "      <td id=\"T_37a40_row17_col3\" class=\"data row17 col3\" >6.91%</td>\n",
       "      <td id=\"T_37a40_row17_col4\" class=\"data row17 col4\" >6.93%</td>\n",
       "      <td id=\"T_37a40_row17_col5\" class=\"data row17 col5\" >6.80%</td>\n",
       "      <td id=\"T_37a40_row17_col6\" class=\"data row17 col6\" >6.33%</td>\n",
       "      <td id=\"T_37a40_row17_col7\" class=\"data row17 col7\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row17_col8\" class=\"data row17 col8\" >5.48%</td>\n",
       "      <td id=\"T_37a40_row17_col9\" class=\"data row17 col9\" >6.31%</td>\n",
       "      <td id=\"T_37a40_row17_col10\" class=\"data row17 col10\" >6.32%</td>\n",
       "      <td id=\"T_37a40_row17_col11\" class=\"data row17 col11\" >6.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_37a40_row18_col0\" class=\"data row18 col0\" >2.02%</td>\n",
       "      <td id=\"T_37a40_row18_col1\" class=\"data row18 col1\" >1.23%</td>\n",
       "      <td id=\"T_37a40_row18_col2\" class=\"data row18 col2\" >1.23%</td>\n",
       "      <td id=\"T_37a40_row18_col3\" class=\"data row18 col3\" >2.03%</td>\n",
       "      <td id=\"T_37a40_row18_col4\" class=\"data row18 col4\" >2.03%</td>\n",
       "      <td id=\"T_37a40_row18_col5\" class=\"data row18 col5\" >2.10%</td>\n",
       "      <td id=\"T_37a40_row18_col6\" class=\"data row18 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row18_col7\" class=\"data row18 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row18_col8\" class=\"data row18 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row18_col9\" class=\"data row18 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row18_col10\" class=\"data row18 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row18_col11\" class=\"data row18 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_37a40_row19_col0\" class=\"data row19 col0\" >7.74%</td>\n",
       "      <td id=\"T_37a40_row19_col1\" class=\"data row19 col1\" >8.41%</td>\n",
       "      <td id=\"T_37a40_row19_col2\" class=\"data row19 col2\" >8.41%</td>\n",
       "      <td id=\"T_37a40_row19_col3\" class=\"data row19 col3\" >7.69%</td>\n",
       "      <td id=\"T_37a40_row19_col4\" class=\"data row19 col4\" >7.71%</td>\n",
       "      <td id=\"T_37a40_row19_col5\" class=\"data row19 col5\" >7.50%</td>\n",
       "      <td id=\"T_37a40_row19_col6\" class=\"data row19 col6\" >6.39%</td>\n",
       "      <td id=\"T_37a40_row19_col7\" class=\"data row19 col7\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row19_col8\" class=\"data row19 col8\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row19_col9\" class=\"data row19 col9\" >6.36%</td>\n",
       "      <td id=\"T_37a40_row19_col10\" class=\"data row19 col10\" >6.37%</td>\n",
       "      <td id=\"T_37a40_row19_col11\" class=\"data row19 col11\" >6.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_37a40_row20_col0\" class=\"data row20 col0\" >3.19%</td>\n",
       "      <td id=\"T_37a40_row20_col1\" class=\"data row20 col1\" >1.80%</td>\n",
       "      <td id=\"T_37a40_row20_col2\" class=\"data row20 col2\" >1.80%</td>\n",
       "      <td id=\"T_37a40_row20_col3\" class=\"data row20 col3\" >3.23%</td>\n",
       "      <td id=\"T_37a40_row20_col4\" class=\"data row20 col4\" >3.21%</td>\n",
       "      <td id=\"T_37a40_row20_col5\" class=\"data row20 col5\" >3.40%</td>\n",
       "      <td id=\"T_37a40_row20_col6\" class=\"data row20 col6\" >6.39%</td>\n",
       "      <td id=\"T_37a40_row20_col7\" class=\"data row20 col7\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row20_col8\" class=\"data row20 col8\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row20_col9\" class=\"data row20 col9\" >6.36%</td>\n",
       "      <td id=\"T_37a40_row20_col10\" class=\"data row20 col10\" >6.37%</td>\n",
       "      <td id=\"T_37a40_row20_col11\" class=\"data row20 col11\" >6.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_37a40_row21_col0\" class=\"data row21 col0\" >4.05%</td>\n",
       "      <td id=\"T_37a40_row21_col1\" class=\"data row21 col1\" >4.52%</td>\n",
       "      <td id=\"T_37a40_row21_col2\" class=\"data row21 col2\" >4.52%</td>\n",
       "      <td id=\"T_37a40_row21_col3\" class=\"data row21 col3\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row21_col4\" class=\"data row21 col4\" >4.06%</td>\n",
       "      <td id=\"T_37a40_row21_col5\" class=\"data row21 col5\" >4.13%</td>\n",
       "      <td id=\"T_37a40_row21_col6\" class=\"data row21 col6\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row21_col7\" class=\"data row21 col7\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row21_col8\" class=\"data row21 col8\" >4.89%</td>\n",
       "      <td id=\"T_37a40_row21_col9\" class=\"data row21 col9\" >4.08%</td>\n",
       "      <td id=\"T_37a40_row21_col10\" class=\"data row21 col10\" >4.07%</td>\n",
       "      <td id=\"T_37a40_row21_col11\" class=\"data row21 col11\" >4.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_37a40_row22_col0\" class=\"data row22 col0\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row22_col1\" class=\"data row22 col1\" >1.34%</td>\n",
       "      <td id=\"T_37a40_row22_col2\" class=\"data row22 col2\" >1.34%</td>\n",
       "      <td id=\"T_37a40_row22_col3\" class=\"data row22 col3\" >1.87%</td>\n",
       "      <td id=\"T_37a40_row22_col4\" class=\"data row22 col4\" >1.86%</td>\n",
       "      <td id=\"T_37a40_row22_col5\" class=\"data row22 col5\" >1.94%</td>\n",
       "      <td id=\"T_37a40_row22_col6\" class=\"data row22 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row22_col7\" class=\"data row22 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row22_col8\" class=\"data row22 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row22_col9\" class=\"data row22 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row22_col10\" class=\"data row22 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row22_col11\" class=\"data row22 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_37a40_row23_col0\" class=\"data row23 col0\" >8.79%</td>\n",
       "      <td id=\"T_37a40_row23_col1\" class=\"data row23 col1\" >11.50%</td>\n",
       "      <td id=\"T_37a40_row23_col2\" class=\"data row23 col2\" >11.50%</td>\n",
       "      <td id=\"T_37a40_row23_col3\" class=\"data row23 col3\" >8.68%</td>\n",
       "      <td id=\"T_37a40_row23_col4\" class=\"data row23 col4\" >8.73%</td>\n",
       "      <td id=\"T_37a40_row23_col5\" class=\"data row23 col5\" >8.34%</td>\n",
       "      <td id=\"T_37a40_row23_col6\" class=\"data row23 col6\" >6.39%</td>\n",
       "      <td id=\"T_37a40_row23_col7\" class=\"data row23 col7\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row23_col8\" class=\"data row23 col8\" >6.59%</td>\n",
       "      <td id=\"T_37a40_row23_col9\" class=\"data row23 col9\" >6.36%</td>\n",
       "      <td id=\"T_37a40_row23_col10\" class=\"data row23 col10\" >6.37%</td>\n",
       "      <td id=\"T_37a40_row23_col11\" class=\"data row23 col11\" >6.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37a40_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_37a40_row24_col0\" class=\"data row24 col0\" >1.63%</td>\n",
       "      <td id=\"T_37a40_row24_col1\" class=\"data row24 col1\" >1.76%</td>\n",
       "      <td id=\"T_37a40_row24_col2\" class=\"data row24 col2\" >1.76%</td>\n",
       "      <td id=\"T_37a40_row24_col3\" class=\"data row24 col3\" >1.65%</td>\n",
       "      <td id=\"T_37a40_row24_col4\" class=\"data row24 col4\" >1.64%</td>\n",
       "      <td id=\"T_37a40_row24_col5\" class=\"data row24 col5\" >1.73%</td>\n",
       "      <td id=\"T_37a40_row24_col6\" class=\"data row24 col6\" >1.84%</td>\n",
       "      <td id=\"T_37a40_row24_col7\" class=\"data row24 col7\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row24_col8\" class=\"data row24 col8\" >1.69%</td>\n",
       "      <td id=\"T_37a40_row24_col9\" class=\"data row24 col9\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row24_col10\" class=\"data row24 col10\" >1.85%</td>\n",
       "      <td id=\"T_37a40_row24_col11\" class=\"data row24 col11\" >1.92%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x177563c10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
